In statistics, a misleading graph, also known as a distorted graph, is a graph that misrepresents data, constituting a misuse of statistics and with the result that an incorrect conclusion may be derived from it.

Graphs may be misleading through being excessively complex or poorly constructed. Even when constructed to accurately display the characteristics of their data, graphs can be subject to different interpretation.[1]

Misleading graphs may be created intentionally to hinder the proper interpretation of data or accidentally due to unfamiliarity with graphing software, misinterpretation of data, or because data cannot be accurately conveyed. Misleading graphs are often used in false advertising. One of the first authors to write about misleading graphs was Darrell Huff, publisher of the 1954 book How to Lie with Statistics.

The field of data visualization describes ways to present information that avoids creating misleading graphs.

The use of graphs where they are not needed can lead to unnecessary confusion/interpretation.[4] Generally, the more explanation a graph needs, the less the graph itself is needed.[4] Graphs do not always convey information better than tables.[5]

Comparing pie charts of different sizes could be misleading as people cannot accurately read the comparative area of circles.[7]

The usage of thin slices, which are hard to discern, may be difficult to interpret.[7]

The usage of percentages as labels on a pie chart can be misleading when the sample size is small.[8]

Making a pie chart 3D or adding a slant will make interpretation difficult due to distorted effect of perspective.[9] Bar-charted pie graphs in which the height of the slices is varied may confuse the reader.[9]

A perspective(3D) pie chart is used to give the chart a 3D look. Often used for aesthetic reasons, the third dimension does not improve the reading of the data; on the contrary, these plots are difficult to interpret because of the distorted effect of perspective associated with the third dimension. The use of superfluous dimensions not used to display the data of interest is discouraged for charts in general, not only for pie charts.[10] In a 3D pie chart, the slices that are closer to the reader appear to be larger than those in the back due to the angle at which they're presented.[11]

Comparison of pie charts

Misleading pie chart

Regular pie chart

In the misleading pie chart, Item C appears to be at least as large as Item A, whereas in actuality,
it is less than half as large.

Tables are preferable to graphics for many small data sets. A table is nearly always better than a dumb pie chart; the only thing worse than a pie chart is several of them, for then the viewer is asked to compare quantities located in spatial disarray both within and between pies – Given their low data-density and failure to order numbers along a visual dimension, pie charts should never be used.[5]

When using pictograms in bar graphs, they should not be scaled uniformly, as this creates a perceptually misleading comparison.[12] The area of the pictogram is interpreted instead of only its height or width.[13] This causes the scaling to make the difference appear to be squared.[13]

Improper scaling of 2D pictogram in bar graph

Improper Scaling

Regular

Comparison

In the improperly scaled pictogram bar graph, the image for B is actually 9 times as large as A.

2D shape scaling comparison

Square

Circle

Triangle

The perceived size increases when scaling.

The effect of improper scaling of pictograms is further exemplified when the pictogram has 3 dimensions, in which case the effect is cubed.[14]

The usage of improper scaling of a three-dimensional pictogram in this fictitious graph. It appears that home sales have gone up significantly in 2001 over the previous year. Additionally, because no frequency axis is supplied, readers are unable to quantify the change and are only left with a misleading perception of the change. The scaling, which is 2×, causes the change to appear to be 22, or 4 times as large.

Additionally, an improperly scaled pictogram may leave the reader with the sense that the item itself has actually changed in size.[15]

Misleading

Regular

Assuming the pictures represent equivalent quantities, the misleading graph makes it appear that there are more bananas
because the bananas occupy the most area and are furthest to the right.

Logarithmic (often referred to as log) scales can be a very valid means of representing data, however if used without clearly being labelled as so, or displayed to a reader who is unfamiliar with the concept of log scales, a graph using a log scale can be misleading. Log scales put the data values in terms of a chosen number (the base of the log) to a particular power, the base is often e (2.71828...) or 10. For example, log scales may give a height of 1 "unit" for a value of 10 in the data and a height of 6 "units" for a value of 1000000 (1x10^6) in the data. Log scales have common usage in some fields, including the VEI (volcanic explosivity index) scale or the richter scale for earthquakes, magnitudes of stars in astronomy and the pH of acidic and alkaline solutions are also based on a form of log scale, but can have the effect of making data less immediately apparent to the eye. Often the reason for the use of log scales is that the graph's author wishes to display vastly effects of vastly different scales on the same axis, without log scales comparing quantities such as 10^3 versus 10^9 becomes impractical from a visual perspective. A graph with a log scale which was not clearly labelled as such, or a graph with a log scale presented to a viewer who did not have knowledge of logarithmic scales, would generally result in a representation which made data values look of similar size whilst in fact being of widely differing magnitudes. Misuse of a log scale can make vastly different values (such as 10 and 10 thousand) appear close together (on a log scale they would be only "1" and "4" ), or it can make small values appear to be negative due to the way in which logarithmic scales represent numbers smaller than the chosen value used as their base.

Misuse of log scales may also cause relationships between quantities to appear to be linear whilst those relationships are in fact exponentials or power laws which rise very rapidly towards higher values. It has been stated, although mainly in a humorous way, that "anything looks linear on a log-log plot with thick marker pen" .

A truncated graph (also known as a torn graph) has a y axis that does not start at 0. These graphs can create the impression of important change where there is relatively little change.

Truncated graphs are useful in illustrating small differences.[16] Graphs may also be truncated to save space.[16] Commercial software such as MS Excel will tend to truncate graphs by default if the values are all within a narrow range, as in this example.

Truncated bar graph

Truncated bar graph

Regular bar graph

Both of these graphs display identical data; however, in the truncated bar graph on the left,
the data appear to show significant differences, whereas in the regular bar graph on the right, these differences are hardly visible.

The scales of a graph are often used to exaggerate or minimize differences.[17][18]

Misleading bar graph with no scale

Less difference

More difference

The lack of a starting value for the y axis makes it unclear whether the graph is truncated. Additionally, the lack of tick marks prevents the reader from determining whether the graph bars are properly scaled. Without a scale, the visual difference between the bars can be easily manipulated.

Misleading line graph with no scale

Volatility

Steady, fast growth

Slow growth

Though all three graphs share the same data, and hence the actual slope of the (x, y) data is the same, the way that the data is plotted can change the visual appearance of the angle made by the line on the graph. This is because each plot has different scale on its vertical axis. Because the scale is not shown, these graphs can be misleading.

A graph with a high lie factor (>1) would exaggerate change in the data it represents, while one with a small lie factor (>0, <1) would obscure change in the data.[22] A perfectly accurate graph would exhibit a lie factor of 1.

The graph discrepancy index, also known as the graph distortion index (GDI), was originally proposed by Paul John Steinbart in 1998. GDI is calculated as a percentage ranging from −100% to positive infinity, with zero percent indicating that the graph has been properly constructed and anything outside the ±5% margin is considered to be distorted.[20] Research into the usage of GDI as a measure of graphics distortion has found it to be inconsistent and discontinuous, making the usage of GDI as a measurement for comparisons difficult.[20]

data-ink ratio=“ink” used to display the datatotal “ink” used to display the graphic{\displaystyle {\text{data-ink ratio}}={\frac {\text{“ink” used to display the data}}{\text{total “ink” used to display the graphic}}}}

The data-ink ratio should be relatively high, otherwise the chart may have unnecessary graphics.[22]

Several published studies have looked at the usage of graphs in corporate reports for different corporations in different countries and have found frequent usage of improper design, selectivity, and measurement distortion within these reports.[25][26][27][28][29][30][31] The presence of misleading graphs in annual reports have led to requests for standards to be set.[19][32][33][34]

Research has found that while readers with poor levels of financial understanding have a greater chance of being misinformed by misleading graphs,[35] even those with financial understanding, such as loan officers, may be misled.[32]

^Frownfelter-Lohrke, Cynthia; Fulkerson, C. L. (1 July 2001). "The Incidence and Quality of Graphics in Annual Reports: An International Comparison". Journal of Business Communication. 38 (3): 337–357. doi:10.1177/002194360103800308.

Beattie, Vivien; Jones, Michael John (May 2001). "A six-country comparison of the use of graphs in annual reports". The International Journal of Accounting. 36 (2): 195–222. doi:10.1016/S0020-7063(01)00094-2.